Title:	Real-time Light Estimation and Neural Soft Shadows for AR Indoor Scenarios
Authors:	Sommer, Alexander Schwanecke, Ulrich Schoemer, Elmar
Citation:	Journal of WSCG. 2023, vol. 31, no. 1-2, p. 71-79.
Issue Date:	2023
Publisher:	Václav Skala - UNION Agency
Document type:	článek article
URI:	http://hdl.handle.net/11025/54286
ISSN:	1213 – 6972 (hard copy) 1213 – 6980 (CD-ROM) 1213 – 6964 (on-line)
Keywords:	rozšířená realita;lehký odhad;vykreslování stínů;neurální měkké stíny
Keywords in different language:	augmented reality;light estimation;shadow rendering;neural soft shadows
Abstract in different language:	We present a pipeline for realistic embedding of virtual objects into footage of indoor scenes with focus on real-time AR applications. Our pipeline consists of two main components: A light estimator and a neural soft shadow texture generator. Our light estimation is based on deep neural nets and determines the main light direction, light color, ambient color and an opacity parameter for the shadow texture. Our neural soft shadow method encodes object-based realistic soft shadows as light direction dependent textures in a small MLP. We show that our pipeline can be used to integrate objects into AR scenes in a new level of realism in real-time. Our models are small enough to run on current mobile devices. We achieve runtimes of 9ms for light estimation and 5ms for neural shadows on an iPhone 11 Pro.
Rights:	© Václav Skala - UNION Agency
Appears in Collections:	Volume 31, Number 1-2 (2023)

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.